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The study of somatic embryogenesis can provide insight into early plant development. We previously obtained LaMIR166a-overexpressing embryonic cell lines of Larix kaempferi (Lamb.) Carr. To further elucidate the molecular mechanisms associated with miR166 in this species, the transcriptional profiles of wild-type (WT) and three LaMIR166a-overexpressing transgenic cell lines were subjected to RNA sequencing using the Illumina NovaSeq 6000 system. In total, 203,256 unigenes were generated using Trinity de novo assembly, and 2467 differentially expressed genes were obtained by comparing transgenic and WT lines. In addition, we analyzed the cleaved degree of LaMIR166a target genes LaHDZ31–34 in different transgenic cell lines by detecting the expression pattern of LaHdZ31–34, and their cleaved degree in transgenic cell lines was higher than that in WT. The downstream genes of LaHDZ31–34 were identified using Pearson correlation coefficients. Yeast one-hybrid and dual-luciferase report assays revealed that the transcription factors LaHDZ31–34 could bind to the promoters of LaPAP, LaPP1, LaZFP5, and LaPHO1. This is the first report of gene expression changes caused by LaMIR166a overexpression in Japanese larch. These findings lay a foundation for future studies on the regulatory mechanism of miR166.

A simple and efficient protocol for the genetic transformation of Japanese larch (Larix kaempferi (Lamb.) Carr.) was developed by altering the infection method and duration and the bacterial removal process. More than 600 hygromycin-resistant embryonal masses with the vector pCAMBIA1301 were obtained, with an average of 20 transgenic lines per gram of fresh weight. Nine hygromycin-resistant transformation events (designated P1–P9) were analyzed using PCR, quantitative real-time PCR, and histochemical β-glucuronidase (GUS) assays. The GUS transcript abundance in each transformed cell line ranged from 101 to 103 magnitudes, with a maximum abundance of 2.89 × 103. In addition, the pLaTCTP::GUS vector, which contains GUS under the control of the L. kaempferi LaTCTP promoter, led to constitutive expression of GUS in embryonal-suspensor mass and somatic embryos. The transcript abundance of the exogenous genes HPT and GUS, driven by the CaMV 35S or LaTCTP promoter, ranged from 101 to 104, which was equivalent to genes with moderate and low abundances in Japanese larch. The relatively low expression levels of exogenous genes in transformants might reflect the large genome of Japanese larch. Additional transgenic cell lines need to be screened to obtain transformants with higher expression levels of foreign genes for further functional research in Japanese larch.

Chinese wolfberry (Lycium spp.) is an important edible and medicinal plant, with a long cultivation history. The genetic relationships among wild Lycium species and landraces have been unclear for a number of reasons, which has hindered the breeding of modern Chinese wolfberry cultivars. In this study, we collected 19 accessions of Chinese wolfberry germplasm, and constructed the genetic relationship based on RAD-seq markers. We obtained 30.32 Gb of clean data, with the average value of each sample being 1.596 Gb. The average mapping rate was 85.7%, and the average coverage depth was 6.76 X. The phylogeny results distinguished all accessions clearly. All the studied landraces shared their most recent common ancestor with L. barbarum, which indicated that L. barbarum may be involved in cultivation of these landraces. The relationship of some landraces, namely the ‘Ningqi’ series, ‘Qingqi-1’ and ‘Mengqi-1,’ has been supported by the phylogeny results, while the triploid wolfberry was shown to be based on a hybrid between ‘Ningqi-1’ and a tetraploid wolfberry. This study uncovered the genetic background of Chinese wolfberry, and developed the foundation for species classification, accession identification and protection, and the production of hybrid cultivars of wolfberry.

A simple and efficient protocol for the genetic transformation of Japanese larch (Larix kaempferi (Lamb.) Carr.) was developed by altering the infection method and duration and the bacterial removal process. More than 600 hygromycin-resistant embryonal masses with the vector pCAMBIA1301 were obtained, with an average of 20 transgenic lines per gram of fresh weight. Nine hygromycin-resistant transformation events (designated P1–P9) were analyzed using PCR, quantitative real-time PCR, and histochemical β-glucuronidase (GUS) assays. The GUS transcript abundance in each transformed cell line ranged from 10[sup.1] to 10[sup.3] magnitudes, with a maximum abundance of 2.89 × 10[sup.3] . In addition, the pLaTCTP::GUS vector, which contains GUS under the control of the L. kaempferi LaTCTP promoter, led to constitutive expression of GUS in embryonal-suspensor mass and somatic embryos. The transcript abundance of the exogenous genes HPT and GUS, driven by the CaMV 35S or LaTCTP promoter, ranged from 10[sup.1] to 10[sup.4] , which was equivalent to genes with moderate and low abundances in Japanese larch. The relatively low expression levels of exogenous genes in transformants might reflect the large genome of Japanese larch. Additional transgenic cell lines need to be screened to obtain transformants with higher expression levels of foreign genes for further functional research in Japanese larch.

PurposePrevious knowledge base question answering (KBQA) models only consider the monolingual scenario and cannot be directly extended to the cross-lingual scenario, in which the language of questions and that of knowledge base (KB) are different. Although a machine translation (MT) model can bridge the gap through translating questions to the language of KB, the noises of translated questions could accumulate and further sharply impair the final performance. Therefore, the authors propose a method to improve the robustness of KBQA models in the cross-lingual scenario.Design/methodology/approachThe authors propose a knowledge distillation-based robustness enhancement (KDRE) method. Specifically, first a monolingual model (teacher) is trained by ground truth (GT) data. Then to imitate the practical noises, a noise-generating model is designed to inject two types of noise into questions: general noise and translation-aware noise. Finally, the noisy questions are input into the student model. Meanwhile, the student model is jointly trained by GT data and distilled data, which are derived from the teacher when feeding GT questions.FindingsThe experimental results demonstrate that KDRE can improve the performance of models in the cross-lingual scenario. The performance of each module in KBQA model is improved by KDRE. The knowledge distillation (KD) and noise-generating model in the method can complementarily boost the robustness of models.Originality/valueThe authors first extend KBQA models from monolingual to cross-lingual scenario. Also, the authors first implement KD for KBQA to develop robust cross-lingual models.

Relation extraction, a crucial task in natural language processing (NLP) for constructing knowledge graphs, entails extracting relational semantics between pairs of entities within a sentence. Given the intricacy of language, a single sentence often encompasses multiple entities that mutually influence one another. Recently, various iterations of recurrent neural networks (RNNs) have been introduced into relation extraction tasks, where the efficacy of neural network structures directly influences task performance. However, many neural networks necessitate manual determination of optimal parameters and network architectures, resulting in limited generalization capabilities for specific tasks. In this paper, we formally define the context-based relation extraction problem and propose a solution utilizing neural architecture search (NAS) to optimize RNN. Specifically, NAS employs an RNN controller to delineate an RNN cell, yielding an optimal structure to represent all relationships, thereby aiding in extracting relationships between target entities. Additionally, to enhance relation extraction performance, we leverage the XLNet pretrained model to comprehensively capture the semantic features of the sentence. Extensive experiments conducted on a real-world dataset containing words with multiple relationships demonstrate that our proposed method significantly enhances micro-F1 scores compared to state-of-the-art baselines.